The present invention relates to process oversight systems and, more particularly, to methods and an apparatus for the early detection and warning of an abnormal situation or irregular trend occurring in a multi-variable process. The invention also permits a user to analyze the causative factor(s) of an abnormal situation and to predict the appropriate maintenance time for elements of a process.
Multi-variable processes are characterized by a large number of varying parameters or elements which influence the output. Process parameters may consist of any of the measurable or calculable variables of a process. For example, an automobile has a number of engine related parameters such as coolant temperature, oil pressure, exhaust gas content levels, spark intensity and the like that will indicate whether the engine is functioning correctly or is operating within a level of dysfunction. An aircraft, as another example, has many more. Accordingly, it is common practice to monitor each critical parameter of a process or a system in order to detect an incipient dysfunction before that discrete dysfunction causes a system dysfunction, i.e., a breakdown of the engine.
Such parameters are either continuously or periodically monitored by sensors. Each parameter has its own defined limit, some being between a certain high and low, others greater than a certain threshold value and others lower than a certain threshold value. Exceeding the limit indicates dysfunction and typically triggers an alert signal, such as a red light, a buzzer, a gauge needle entering a defined area or exceeding a red line, etc. These parameters are monitored individually and their dysfunction alerts are activated without regard to the status of other parameters that could, in fact, be functionally related.
In reality, relationships often exist between the various parameters of a process, and methods of monitoring and measuring such interrelated parameters are commonly in use. Such multivariate Statistical Process Control (SPC) methods are known in the prior art. However, such methods suffer from a number of shortcomings, such as the following:
1. Alerts are generated as a result of a negative occurrence during one of the stages of a multi-stage process, but often no indication is given regarding the primary source of the dysfunction that led to the alert. Thus, problems are frequently identified too late and even then, no analysis can be done after the alert generation.
2. Interactions are typically not well addressed. When dealing with multiple factors, multi-variate analysis or logic based systems are used. However, while interactions can be evaluated, the reality is that they rarely are, as potentially too many exist.
3. Most multivariate models, like those created using linear regression or logical regression, assume that a linear relationship applies among variables. These assumptions are not always correct.
4. The alert limit indicating dysfunction for each parameter is usually constant and does not vary even though the relationship of the parameter to other variables of the same process will cause the limit to be variable, thus causing an alert to signal an alarm when, in fact, there is no dysfunction and, similarly, to not signal when there is a dysfunction.
U.S. Pat. No. 5,768,119 to Havekost teaches an SPC system including alert priority adjustment. The system includes an alert and event monitoring and display application which users can easily prioritize. The system monitors and uniformly displays diagnostic information on processes comprising different devices. The invention is particularly useful for prioritizing various alerts but does not relate to the causes of the alerts nor to preventative measures that can be taken by early detection.
U.S. Pat. No. 5,949,677 to Ho teaches an improved SPC with fault detection and correction capabilities. A redundant control architecture which includes a primary control system and a monitor control system is provided, with each control system generating a control signal. The difference between the two control signals is monitored by a fault detection system. The fault detection system comprises an integrator and a memory capable of recording signal differences for a predetermined period of time. The use of memory allows signal differences to be added to the integrator and subtracted at a later time. This invention is useful for eliminating noise effects but does not relate to the causes of the alerts nor to preventative measures that can be taken by early detection.
U.S. Pat. No. 6,314,328 to Powell teaches an alert generation method which allows pinpointing the parameter that caused the alert but does not relate to other contributory factors.
There is thus a widely recognized need for, and it would be highly advantageous to have, a method of providing an early warning of an abnormal situation in a multi-variate process devoid of the above limitations.